Search Results (535)

Using the rolling-shutter mechanism to enhance the signal sampling frequency of Optical Camera Communication (OCC) is a low-cost solution, but its periodic sampling interruptions may cause signal loss, and existing solutions often compromise communication rate and distance. To address this, this paper proposes NoGap-RS, a no-gap sampling method, theoretically addressing the signal loss issue at longer distances from a perspective of CMOS exposure timing. Experiments show that NoGap-OOK, a OCC system based on NoGap-RS and On-Off key modulation, can achieve a communication rate of 6.41 Kbps at a distance of 3 m, with a BER of ${10}^{-5}$ under indoor artificial light. This paper further proposes TickRS, a time slot division method, innovatively addressing the overlap that occurs during consecutive-row exposures to further enhance communication rate. Experiments show that TickRS-CSK, a OCC system based on TickRS and Color-Shift Key, can achieve a communication rate of 20.09 Kbps at a distance of 3.6 m, with a BER of ${10}^{-2}$ under indoor natural light. Full article

This paper studied the general mechanism of spatial-frequency-shift (SFS) super-resolution imaging based on multiplex illumination modulation. The theory of SFS joint intensity was first proposed. Experiments on parallel slots with discrete spatial frequency (SF) distribution and V-shape slots with continuous SF distribution were carried out, and their real-space images and k-space images were obtained. The influence of single illumination with different SFS and mixed illumination with various combinations on SFS super-resolution imaging was analyzed. The phenomena of sample SF coverage were discussed. The SFS super-resolution imaging characteristics based on low-coherence illumination and highly localized light fields were discovered. The phenomenon of image magnification during SFS super-resolution imaging process was discussed. The differences and connections between the SF spectrum of objects and the k-space images obtained in SFS super-resolution imaging process were explained. This provides certain support for optimization of high-throughput SFS super-resolution imaging. Full article

Orthogonal time–frequency space (OTFS) modulation has emerged as a promising technology to alleviate the effects of the Doppler shifts in high-mobility environments. As a prerequisite to demodulation and signal processing, automatic modulation classification (AMC) is essential for OTFS systems. However, a very limited number of works have focused on this issue. In this paper, we propose a novel AMC approach for OTFS systems. We build a dual-stream convolutional neural network (CNN) model to simultaneously capture multi-domain signal features, which substantially enhances recognition accuracy. Moreover, we propose a differentiated embedded pilot structure that incorporates information about distinct modulation schemes to further improve the separability of modulation types. The results of the extensive experiments carried out show that the proposed approach can achieve high classification accuracy even under low signal-to-noise ratio (SNR) conditions and outperform the state-of-the-art baselines. Full article

Special vehicle detection in intelligent surveillance, emergency rescue, and reconnaissance faces significant challenges in accuracy and robustness under complex environments, necessitating advanced detection algorithms for critical applications. This paper proposes HSF-DETR (Hypergraph Spatial Feature DETR), integrating four innovative modules: a Cascaded Spatial Feature Network (CSFNet) backbone with Cross-Efficient Convolutional Gating (CECG) for enhanced long-range detection through hybrid state-space modeling; a Hypergraph-Enhanced Spatial Feature Modulation (HyperSFM) network utilizing hypergraph structures for high-order feature correlations and adaptive multi-scale fusion; a Dual-Domain Feature Encoder (DDFE) combining Bipolar Efficient Attention (BEA) and Frequency-Enhanced Feed-Forward Network (FEFFN) for precise feature weight allocation; and a Spatial-Channel Fusion Upsampling Block (SCFUB) improving feature fidelity through depth-wise separable convolution and channel shift mixing. Experiments conducted on a self-built special vehicle dataset containing 2388 images demonstrate that HSF-DETR achieves mAP50 and mAP50-95 of 96.6% and 70.6%, respectively, representing improvements of 3.1% and 4.6% over baseline RT-DETR while maintaining computational efficiency at 59.7 GFLOPs and 18.07 M parameters. Cross-domain validation on VisDrone2019 and BDD100K datasets confirms the method’s generalization capability and robustness across diverse scenarios, establishing HSF-DETR as an effective solution for special vehicle detection in complex environments. Full article

The demand for versatility and finer resolution drives phased array radars to develop towards multi-band operating. However, the bandwidth limitations of conventional electronic devices make multi-band manipulation of frequency and phase rather challenging. This paper introduces a joint phase–frequency distribution manipulation method. By introducing a time delay line after optical pulses, the frequency conversion and phase shift are tightly coupled. Then, the phase–frequency–time mapping for multi-band signals in a single phased array system is established. The generation, transmission, and reception of multi-band signals are simultaneously achieved. Our approach enables multi-band frequency conversion and phase shifting in a single hardware framework, ensuring synchronization and coherence across multiple bands. We experimentally demonstrate the generation, frequency conversion, and phase control of signals across four bands (S, X, Ku, and K). Beamforming and data fusion of four-band linear frequency-modulated signals with a total bandwidth of 4 GHz are achieved, resulting in a four-fold improvement in range resolution. It is also verified that the number of bands and total bandwidth can be further expanded through channel interleaving. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

Background: Chronic pain is closely associated with dysregulation of the autonomic nervous system, often reflected by reduced heart rate variability (HRV). While observational studies have demonstrated this association, the extent to which pain interventions modulate HRV and the impact of individual factors on HRV changes remain unclear. Objective: To evaluate the impact of pain interventions on HRV parameters through meta-analysis of randomized controlled trials (RCTs), and to examine whether intervention type and individual factors such as body mass index (BMI) moderate HRV responses. Methods: We conducted a systematic review of 23 RCTs and a meta-analysis of 21 RCTs (1262 subjects) involving patients with acute and chronic pain. HRV outcomes were extracted pre- and post-intervention. Both between-group (active vs. sham/control) and one-group (pre-post within active group) analyses were performed for time-domain indices—standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), and percentage of successive normal-to-normal intervals > 50 ms (pNN50)—and frequency-domain indices—high-frequency (HF) and low-frequency (LF) components. Meta-regressions tested moderators including BMI, age, and pain phenotype. The protocol was registered in PROSPERO (CRD42023448264). Results: Twenty-three RCTs involving 1262 participants with a wide range of pain conditions were included. Meta-analysis of time-domain HRV parameters showed a trend toward improvement: SDNN (g = 0.435, p = 0.059) approached significance, while RMSSD (g = 0.361, p = 0.099) and pNN50 (g = 0.222, p = 0.548) showed smaller, non-significant effects. Frequency-domain analysis revealed a significant moderate reduction in the LF/HF ratio (g = −0.378, p = 0.003), suggesting a shift toward parasympathetic dominance. HF and LF showed small, non-significant changes. One-group meta-analysis confirmed significant improvements in vagally mediated HRV, with large effects for RMSSD (g = 1.084, p < 0.001) and HF (g = 0.622, p < 0.001), and a moderate effect for SDNN (g = 0.455, p = 0.004). Meta-regression identified BMI as a significant moderator: higher BMI was associated with attenuated improvements in HF and RMSSD and a slight shift toward sympathetic predominance. Conclusions: Pain interventions can significantly modulate autonomic function, as reflected in HRV improvements, particularly in vagally mediated indices. These effects are influenced by patient characteristics such as BMI. HRV may serve as a valuable biomarker for both treatment efficacy and autonomic recovery in pain management. In this context, HRV highlights its role as a biomarker for pain dysregulation and compensatory failure, reflecting shared top-down modulation between nociception and autonomic regulation. Full article

Vibrational analysis of peptides in solution and the theoretical determination of the effects of the microenvironment on infrared and Raman spectra are of key importance in many fields of chemical interest. In this work, we present a computational study combining static quantum mechanical calculations with ab initio molecular dynamics simulations to investigate the vibrational behavior of three peptide models in both the gas phase and in explicit water, under non-periodic boundary conditions. The vibrational spectra of the main amide bands, namely amide I-III and A, were analyzed using a time–frequency approach based on the wavelet transform, which allows the resolution of transient frequency shifts and mode couplings along the trajectories. This combined approach enabled us to perform a time-resolved vibrational analysis revealing how vibrational frequencies, especially of the C=O and N–H stretching modes, evolve over time due to dynamical microsolvation. These fluctuations modulate vibrational couplings and lead to spectral broadening and frequency shifts that correlate with the local structuring of the solvent. In conclusion, our results highlight how the proposed protocol allows for the direct connection between vibrational modes and local structural changes, providing a link from the spectroscopic observable to the structure, the peptide backbone, and its microenvironment. Full article

Manhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex environment of manhole covers. To address these challenges, an intelligent management model based on the improved YOLOv8 model is proposed for three types of urban high-frequency defects: “breakage, loss and shift”. We design a lightweight dual-stream feature extraction network and use EfficientNetV2 as the backbone. By introducing the fused MBConv structure, the computational complexity is significantly reduced, while the efficiency of feature extraction is improved. An innovative foreground attention module is introduced to adaptively enhance the features of manhole cover defects, improving the model’s ability to identify defects of various scales. In addition, an optimized feature fusion architecture is constructed by integrating NAS-FPN modules. This structure utilizes bidirectional feature transfer and automatic structure search, significantly enhancing the expressiveness of multi-scale features. A combined loss function design using GIoU loss, dynamically weighted BCE loss, and Distribution Focal Loss (DFL) is adopted to address the issues of sample imbalance and inter-class differences. The experimental results show that the model achieved excellent performance in multiple indicators of manhole cover defect recognition, especially in classification accuracy, recall rate, and F1-score, with an overall recognition accuracy of 98.6%. The application of the improved model in the new smart management system for urban manhole covers can significantly improve management efficiency. Full article

Long Range (LoRa) is one of the promising Low-Power Wide-Area Network technologies to achieve a strong anti-noise ability due to the modulation of the chirp spread spectrum in low-power and long-distance communications. However, LoRa suffers the problem of packet collisions. Hence, we propose QR−LoRa, a novel PHY-layer scheme that can transmit data in both amplitude and frequency dimensions simultaneously. For the amplitude modulation, we modulate the constant envelope of a LoRa chirp with a cyclic right-shifted ramp signal, where the cyclic right-shifted position carries the data of the amplitude modulation. We adopt the standard LoRa for frequency modulation. We prototype QR−LoRa on the software-defined radio platform USRP N210 and evaluate its performance via simulations and field experiments. The results show the bit rate gain of QR−LoRa is up to 2× compared with the standard LoRa device. Full article

Defective phononic crystals (PnCs) have garnered significant attention for their ability to localize and amplify elastic wave energy within defect sites or to perform narrowband filtering at defect-band frequencies. The necessity for continuously tunable defect characteristics is driven by the variable excitation frequencies encountered in rotating machinery. Conventional tuning methodologies, including synthetic negative capacitors or inductors integrated with piezoelectric defects, are constrained to fixed, offline, and incremental adjustments. To address these limitations, the present study proposes an active feedback approach that facilitates online, wide-range steering of defect bands in a one-dimensional PnC. Each defect is equipped with a pair of piezoelectric sensors and actuators, governed by three independently tunable feedback gains: displacement, velocity, and acceleration. Real-time sensor signals are transmitted to a multivariable proportional controller, which dynamically modulates local electroelastic stiffness via the actuators. This results in continuous defect-band frequency shifts across the entire band gap, along with on-demand sensitivity modulation. The analytical model that incorporates these feedback gains has been demonstrated to achieve a level of agreement with COMSOL benchmarks that exceeds 99%, while concurrently reducing computation time from hours to seconds. Displacement- and acceleration-controlled gains yield predictable, monotonic up- or down-shifts in defect-band frequency, whereas the velocity-controlled gain permits sensitivity adjustment without frequency drifts. Furthermore, the combined-gain operation enables the concurrent tuning of both the center frequency and the filtering sensitivity, thereby facilitating an instantaneous remote reconfiguration of bandpass filters. This framework establishes a new class of agile, adaptive ultrasonic devices with applications in ultrasonic imaging, structural health monitoring, and prognostics and health management. Full article

A low-profile dual-polarized shared-aperture phased array antenna is proposed for Ku-band satellite communications in this paper. The stacked octagonal patches loaded with Via-rings are proposed as dual-polarized shared-aperture radiation elements, with the characteristics of wide impedance bandwidth, high gain, and weak coupling. Furthermore, innovative minimized three-port ring couplers are utilized for the differential-fed antenna array, further suppressing the cross-polarization component. Substrate integrated coaxial line (SICL) and microstrip line (MS) feed networks are employed for the excitation of transmitting band (Tx) horizontal polarization and receiving band (Rx) vertical polarization, respectively. The non-uniform subarray architecture is optimized to minimize the sidelobe levels with the reduced number of transmitter and receiver (T/R) radio frequency phase-shifting modules. As proof-of-concept examples, 16 × 24 and 32 × 24 array antennas are demonstrated and fabricated. The measured impedance bandwidths of the proposed phased array antennas are around 21.1%, while the in-band isolations are above 36.7 dB. Gains up to 29 dBi and 32.4 dBi are performed by two prototypes separately. In addition, the T/R phase-shifting modules are utilized to validate the beam-scanning characteristic, which is of value for dynamic satellite communications. Full article

Hydrogen is gaining prominence as a clean energy vector, yet its extreme flammability demands robust detection solutions for industrial safety. In this study, we present the development and experimental validation of a microwave hydrogen gas sensor based on a patch-type microstrip antenna with a silver sensing element. The device operates at 5.99 GHz and was tested under controlled environmental conditions (humidity: 20 ± 0.4%, temperature: 27 ± 0.2 °C). Hydrogen exposure induces measurable shifts in the antenna’s resonant frequency due to dielectric modulation of the silver layer. The sensor exhibited a linear sensitivity of 3 kHz/ppm in the 310–600 ppm concentration range, with a residual standard deviation of 31.1 kHz and a calculated limit of detection (LOD) of approximately 31 ppm. The reflection coefficient remained below −10 dB throughout, confirming that the antenna maintains functional RF performance during sensing. These results demonstrate the sensor’s dual functionality for gas detection and communication, offering a compact and scalable platform for hydrogen safety monitoring. Full article

Proteins are inherently dynamic entities that rely on flexibility across multiple timescales to perform their biological functions. The surrounding environment plays a critical role in modulating protein dynamics by exerting plasticizing or stabilizing effects. In order to characterize the conformational dynamics of Water-Soluble Chlorophyll-Binding Protein (WSCP), we measured Quasielastic Neutron Scattering (QENS) spectra over a wide temperature range between 100 and 300 K. The impact of glycerol, a common stabilizer, is investigated by comparing WSCP dissolved in a glycerol–water-containing buffer (WSCP^W+G) with WSCP in a water-containing buffer (WSCP^W). The results indicate that conformational protein dynamics are widely suppressed below 200 K but increase above this threshold, with the appearance of localized protein motions on the picosecond timescale. Glycerol appears to limit protein mobility between 280 and 300 K due to its high viscosity and hydrogen bonding in contrast to WSCP in water. Inelastic Neutron Scattering (INS) reveals the vibrational dynamics of WSCP with pronounced low-energy protein vibrations observed at about 2.5 and 6 meV. In the presence of glycerol, however, a stiffening of the vibrational motions which shifts the vibrational peaks to higher frequencies is observed. Full article

The Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) communication. This paper is based on the adoption of Hough Transform (HT) and Edge Detection (ED) to enhance modulation classification, especially for a small dataset. Deep neural models based on basic Convolutional Neural Network (CNN), Visual Geometry Group-16 (VGG-16), and VGG-19 trained on constellation diagrams transformed using HT are adopted. The objective is to extract features from constellation diagrams projected onto the Hough space. In addition, we use Orthogonal Frequency Division Multiplexing (OFDM) technology, which is frequently utilized in UWA systems because of its ability to avoid multipath fading and enhance spectrum utilization. We use an OFDM system with the Discrete Cosine Transform (DCT), Cyclic Prefix (CP), and equalization over the UWA communication channel under the effect of estimation errors. Seven modulation types are considered for classification, including Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) (2/8/16-PSK and 4/8/16/32-QAM), with a Signal-to-Noise Ratio (SNR) ranging from −5 to 25 dB. Simulation results indicate that our CNN model with HT and ED at perfect channel estimation, achieves a 94% classification accuracy at 10 dB SNR, outperforming benchmark models by approximately 40%. Full article